Parallelizing passive Radio Frequency Identification (RFID) reading is an arguably crucial, yet unsolved challenge in modern IoT applications. Existing approaches remain limited to time-division operations and fail to read multiple tags simultaneously. In this paper, we introduce QuinID, the first frequency-division multiple access (FDMA) RFID system to achieve fully parallel reading. We innovatively exploit the frequency selectivity of the tag antenna rather than a conventional digital FDMA, bypassing the power and circuitry constraint of RFID tags. Specifically, we delicately design the frequency-selective antenna based on surface acoustic wave (SAW) components to achieve extreme narrow-band response, so that QuinID tags (i.e., QuinTags) operate exclusively within their designated frequency bands. By carefully designing the matching network and canceling various interference, a customized QuinReader communicates simultaneously with multiple QuinTags across distinct bands. QuinID maintains high compatibility with commercial RFID systems and presents a tag cost of less than 10 cents. We implement a 5-band QuinID system and evaluate its performance under various settings. The results demonstrate a fivefold increase in read rate, reaching up to 5000 reads per second.

mmWave-based activity recognition technology has attracted widespread attention as it provides the ability of device-free, ubiquitous and accurate sensing. Recognition of human activities intrinsically demands to be real-time and continuous, but the state of the arts is still far limited with the capacity in this regard. The main obstacle lies in activity sequence segmentation, i.e., locating the boundaries between consecutive activities in an activity sequence. This is a daunting task, due to the unclear activity boundaries and the variable activity duration. In this paper, we proposeZuMa, the first mmWave-based approach to real-time continuous activity recognition. When resorting to a machine learning model for activity recognition, our insight is that the recognition confidence of the recognition model is highly correlated to the accuracy of activity sequence segmentation, so that the former can be utilized as a feedback metric to finely adjust the segmentation boundaries. Based on this insight,ZuMais a coarse-to-fine grained approach, which includes the fast coarse-grained activity chunk extraction and the find-grained explicit segmentation adjustment and recognition. We have implementedZuMawith the commercial mmWave radar and evaluated its performance under various settings. The results demonstrate thatZuMaachieves an average recognition error of 12.67%, which is 65.08% and 71.87% lower than that of the two baseline methods. The average recognition delay ofZuMais only 1.86 s.

Backscatter is an enabling technology for battery-free sensing in today’s Artificial Intelligence of Things (AIOT). Building a backscatter sensing system, however, is a daunting task, due to two obstacles: the unaffordable power consumption of the microprocessor and the coexistence with the ambient carrier’s traffic. In order to address the issues, we present Leggiero, the first-of-its-kind analog WiFi backscatter with payload transparency, and its enhanced version, Leggiero+. A specially designed circuit based on the varactor diode directly converts fast-varying analog sensor signals into the RF (radio frequency) signal phase, eliminating the need for a microprocessor to interface between the radio and the sensor. By precisely locating the WiFi packet’s extra long training field (LTF) section and carefully designing the reference circuit, Leggiero embeds the analog phase into the channel state information (CSI). A commodity WiFi receiver without hardware modification can simultaneously decode the WiFi and the sensor data. We implement and evaluate Leggiero and Leggiero+ under varied settings. Results show the tag’s power consumption (excluding the power of the peripheral sensor module) is$30\mu $W at a 400Hz sampling rate,$4.8\times $and$4\times $lower than the state-of-the-art WiFi backscatter schemes. Leggiero+ demonstrates enhanced throughput, communication range, and analog signal reproduction accuracy. Our design supports a variety of sensing applications, while maintaining the WiFi carrier’s throughput performance.

Wave-based human tracking is a key enabling technology for smart applications. Most of the existing works on this topic employ the conventional approach of device-free object localization, which treat any person as a general moving target rather than distinguish different persons. As a result, the existing approaches have poor performance in the scenarios of multi-person tracking, especially when there are crossovers among different persons’ trajectories. This paper presents mMTAI, a novel approach for multi-person tracking with a mmWave radar. By exploiting mmWave sensing to capture a human’s biometric features, MMTAI augments mmWave radar based human tracking with the ability of identifying different persons. Specifically, MMTAI is able to sense persons’ scalp responses to the signals and their head-shoulder distances, which are then continuously mapped to their trajectories using a bipartite matching algorithm. We implement MMTAI with a commercial mmWave radar and evaluate its performance under various settings. The results show that in the multi-person tracking scenarios, mmTAI has a median tracking error of 12.33 cm, which is $35.88 \%$ lower than that of the state-of-the-art approach.

Heart rate recovery (HRR) within the initial minute following exercise is a widely utilized metric for assessing cardiac autonomic function in individuals and predicting mortality risk in patients with cardiovascular disease. However, prevailing solutions for HRR monitoring typically involve the use of specialized medical equipment or contact wearable sensors, resulting in high costs and poor user experience. In this paper, we propose a contactless HRR monitoring technique, mmHRR, which achieves accurate heart rate (HR) estimation with a commercial mmWave radar. Unlike HR estimation at rest, the HR varies quickly after exercise and the heartbeat signal entangles with the respiration harmonics. To overcome these hurdles and effectively estimate the HR from the weak and non-stationary heartbeat signal, we propose a novel signal processing pipeline, including dynamic target tracking, adaptive heartbeat signal extraction, and accurate HR estimation with composite sliding windows. Real-world experiments demonstrate that mmHRR exhibits exceptional robustness across diverse environmental conditions, and achieves an average HR estimation error of 3.31 bpm (beats per minute), 71% lower than that of the state-of-the-art method.

We present Acoustic Inertial Measurement (AIM), a one-of-a-kind technique for indoor drone localization and tracking. Indoor drone localization and tracking are arguably a crucial, yet unsolved challenge: in GPS-denied environments, existing approaches enjoy limited applicability, especially in Non-Line of Sight (NLoS), require extensive environment instrumentation, or demand considerable hardware/software changes on drones. In contrast, AIM exploits the acoustic characteristics of the drones to estimate their location and derive their motion, even in NLoS settings. We tame location estimation errors using a dedicated Kalman filter and the Interquartile Range rule (IQR) and demonstrate that AIM can support indoor spaces with arbitrary ranges and layouts. We implement AIM using an off-the-shelf microphone array and evaluate its performance with a commercial drone under varied settings. Results indicate that the mean localization error of AIM is 46% lower than that of commercial UWB-based systems in a complex 10m×10m indoor scenario, where state-of-the-art infrared systems would not even work because of NLoS situations. When distributed microphone arrays are deployed, the mean error can be reduced to less than 0.5m in a 20m range, and even support spaces with arbitrary ranges and layouts.

Small Unmanned Aerial Vehicles (UAVs) are becoming potential threats to security-sensitive areas and personal privacy. A UAV can shoot photos at height, but how to detect such an uninvited intruder is an open problem. This article presents mmHawkeye, a passive approach for non-cooperative UAV detection and identification with a commercial off-the-shelf millimeter wave (mmWave) radar. mmHawkeye does not require prior knowledge of the type, motions, and flight trajectory of the UAV, while exploiting the signal feature induced by the UAV’s periodic micro-motion (PMM) for long-range accurate detection. The design is therefore effective in dealing with low signal-to-noise ratio and uncertain reflected signals from the UAV. After analyzing the theoretical model of the PMM feature, mmHawkeye can further track the UAV’s position containing range, azimuth and altitude angle with dynamic programming and particle filtering and then identify it with a Long Short-Term Memory–based detector. We implement mmHawkeye on a commercial mmWave radar and evaluate its performance under varied settings. The experimental results show that mmHawkeye has a detection accuracy of 95.8% and can realize detection at a range up to 80 m.

Backscatter is an enabling technology for battery-free sensing in today's Artificial Intelligence of Things (AIOT). Building a backscatter-based sensing system, however, is a daunting task, due to two obstacles: the unaffordable power consumption of the microprocessor and the coexistence with the ambient carrier's traffic. In order to address the above issues, in this paper, we present Leggiero, the first-of-its-kind analog WiFi backscatter with payload transparency. Leveraging a specially designed circuit with a varactor diode, this design avoids using a microprocessor to interface between the radio and the sensor, and directly converts the analog sensor signal into the phase of RF (radio frequency) signal. By carefully designing the reference circuit on the tag and precisely locating the extra long training field (LTF) section of a WiFi packet, Leggiero embeds the analog phase value into the channel state information (CSI). A commodity WiFi receiver without hardware modification can simultaneously decode the WiFi and the sensor data. We implement Leggiero design and evaluate its performance under varied settings. The results show that the power consumption of the Leggiero tag (excluding the power of the peripheral sensor module) is 30μW at a sampling rate of 400Hz, which is 4.8× and 4× lower than the state-of-the-art WiFi backscatter schemes. The uplink throughput of Leggiero is suficient to support a variety of sensing applications, while keeping the WiFi carrier's throughput performance unaffected.

Small Unmanned Aerial Vehicles (UAVs) are becoming potential threats to security-sensitive areas and personal privacy. A UAV can shoot photos at height, but how to detect such an uninvited intruder is an open problem. This paper presents mmHawkeye, a passive approach for UAV detection with a COTS millimeter wave (mmWave) radar. mmHawkeye doesn’t require prior knowledge of the type, motions, and flight trajectory of the UAV, while exploiting the signal feature induced by the UAV’s periodic micro-motion (PMM) for long-range accurate detection. The design is therefore effective in dealing with low-SNR and uncertain reflected signals from the UAV. mmHawkeye can further track the UAV’s position with dynamic programming and particle filtering, and identify it with a Long Short-Term Memory (LSTM) based detector. We implement mmHawkeye on a commercial mmWave radar and evaluate its performance under varied settings. The experimental results show that mmHawkeye has a detection accuracy of 95.8% and can realize detection at a range up to 80m.

Vibration measurement is a crucial task in industrial systems, where vibration characteristics reflect health conditions and indicate anomalies of the devices. Previous approaches either work in an intrusive manner or fail to capture the micrometer-level vibrations. In this work, we propose mmVib, a practical approach to measure micrometer-level vibrations with mmWave radar. First, we derive a metric calledVibration Signal-to-Noise Ratio(VSNR) that highlights the directions of reducing measurement errors of tiny vibrations. Then, we introduce the design of mmVib based on the concept ofMulti-Signal Consolidation(MSC) for the error reduction and multi-object measurement. We implement a prototype of mmVib, and the experiments show that it achieves$3.946\%$relative amplitude error and$0.02487\%$relative frequency error in median. Typically, the average amplitude error is only$3.174um$when measuring the$100um$-amplitude vibration at around 5 meters. Compared to two existing mmWave-based approaches, mmVib reduces the 80th-percentile amplitude error by$69.21\%$and$97.99\%$respectively.

Backscatter communication holds potential for ubiquitous and low-cost connectivity among low-power IoT devices. To avoid interference between the carrier signal and the backscatter signal, recent works propose a frequency-shifting technique to separate these two signals in the frequency domain. Such proposals, however, have to occupy the precious wireless spectrum that is already overcrowded, and increase the power, cost, and complexity of the backscatter tag. In this paper, we revisit the classic ON-OFF Keying (OOK) modulation and propose Aloba, a backscatter system that takes the ambient LoRa transmissions as the excitation and piggybacks the in- band OOK modulated signals over the LoRa transmissions. Our design enables the backscatter signal to work in the same frequency band of the carrier signal, meanwhile achieving flexible data rate at different transmission range. The key contributions of Aloba include: i) the design of a low-power backscatter tag that can pick up the ambient LoRa signals from other signals; ii) a novel decoding algorithm to demodulate both the carrier signal and the backscatter signal from their superposition. We further adopt link coding mechanism and interleave operation to enhance the reliability of backscatter signal decoding. We implement Aloba and conduct head-to-head comparison with the state-of-the-art LoRa backscatter system PLoRa in various settings. The experiment results show Aloba can achieve 39.5–199.4 Kbps data rate at various distances, 10.4–$52.4\times $higher than PLoRa.

Cross-Technology Communication (CTC) emerges as a technology to enable direct communication across different wireless technologies. The state of the art on CTC employs physical-level emulation. Due to the protocol incompatibility and the hardware restriction, there are intrinsic emulation errors between the emulated signals and the legitimate signals. Unresolved emulation errors hurt the reliability of CTC and the achievable throughput, but how to improve the reliability of CTC remains a challenging problem. Taking the CTC from WiFi to BLE as an example, this work first presents a comprehensive understanding of the emulation errors. We then propose WEB, a practical CTC approach that can be implemented with commercial devices. The core design of WEB is split encoding: based on the probabilistic distribution of emulation errors, the WiFi sender manipulates its payload to maximize the successful decoding rate at the BLE receiver. We implement WEB and evaluate its performance with extensive experiments. Compared to two existing approaches, WEBee and WIDE, WEB reduces the SER (Symbol Error Rate) by 54.6% and 42.2%, respectively. For the first time in the community, WEB achieves practically effective CTC from WiFi to BLE, with an average throughput of 522.2 Kbps.

Research on cross-technology communication ( CTC ) has made rapid progress in recent years. While the CTC links are complex and dynamic, how to estimate the quality of a CTC link remains an open and challenging problem. Through our observation and study, we find that none of the existing approaches can be applied to estimate the link quality of CTC. Built upon the physical-level emulation, transmission over a CTC link is jointly affected by two factors: the emulation error and the channel distortion. Furthermore, the channel distortion can be modeled and observed through the signal strength and the noise strength. We, in this article, propose a new link metric called C-LQI and a joint link model that simultaneously takes into account the emulation error and the channel distortion in the In-phase and Quadrature ( IQ ) domain. We accurately describe the superimposed impact on the received signal. We further design a lightweight link estimation approach including two different methods to estimate C-LQI and in turn the packet reception rate ( PRR ) over the CTC link. We implement C-LQI and compare it with two representative link estimation approaches. The results demonstrate that C-LQI reduces the relative estimation error by 49.8% and 51.5% compared with s-PRR and EWMA, respectively.

Cross-Technology Communication (CTC) is an emerging technique that enables direct communication across different wireless technologies. Recent works achieve physical-level CTC by emulating the standard time-domain waveform of the receiver. This method faces the challenges of inherent unreliability due to the imperfect emulation. Different from analog emulation, we propose a novel concept named digital emulation, which stems from the following insight: The receiver relies on phase shift rather than the phase itself to decode signals. Instead of emulating the original time-domain waveform, the sender emulates the phase shift associated with the desired signals. Clearly there are multiple different phase sequences that correspond to the same signs of phase shifts. Digital emulation has flexibility in setting the phase values in the emulated signals, which is effective in reducing emulation errors and enhancing the reliability of CTC. The key point of digital emulation is generic and applicable to a set of CTCs, where the transmitter has a wider bandwidth for emulation and the receiver decoding is based on the phase shift. In this paper, we implement our proposal as WIDE, a physical-level CTC via digital emulation from WiFi to ZigBee. We conduct extensive experiments to evaluate the performance of WIDE. The results show that WIDE significantly improves the Packet Reception Ratio (PRR) from 41.7% to 86.2%, which is 2× of WEBee's, an existing representative physical-level CTC.

Research on Cross-technology communication (CTC) has made rapid progress in recent years, but how to estimate the quality of a CTC link remains an open and challenging problem. Through our observation and study, we find that none of the existing approaches can be applied to estimate the link quality of CTC. Built upon the physical-level emulation, transmission over a CTC link is jointly affected by two factors: the emulation error and the channel distortion. We in this paper propose a new link metric called C-LQI and a joint link model that simultaneously takes into account the emulation error and the channel distortion in the process of CTC. We further design a light-weight link estimation approach to estimate C-LQI and in turn the PRR over the CTC link. We implement C-LQI and compare it with two representative link estimation approaches. The results demonstrate that C-LQI reduces the relative error of link estimation respectively by 46% and 53% and saves the communication cost by 90%.

Backscatter communication holds potential for ubiquitous and low-cost connectivity among low-power IoT devices. To avoid interference between the carrier signal and the backscatter signal, recent works propose a frequency-shifting technique to separate these two signals in the frequency domain. Such proposals, however, have to occupy the precious wireless spectrum that is already overcrowded, and increase the power, cost, and complexity of the backscatter tag. In this paper, we revisit the classic ON-OFF Keying (OOK) modulation and propose Aloba, a backscatter system that takes the ambient LoRa transmissions as the excitation and piggybacks the in-band OOK modulated signals over the LoRa transmissions. Our design enables the backsactter signal to work in the same frequency band of the carrier signal, meanwhile achieving good tradeoff between transmission range and link throughput. The key contributions of Aloba include: i) the design of a low-power backscatter tag that can pick up the ambient LoRa signals from other signals; ii) a novel decoding algorithm to demodulate both the carrier signal and the backscatter signal from their superposition. The design of Aloba completely unleashes the backscatter tag's ability in OOK modulation and achieves flexible data rate at different transmission range. We implement Aloba and conduct head-to-head comparison with the state-of-the-art LoRa backscatter system PLoRa in various settings. The experiment results show Aloba can achieve 39.5--199.4 Kbps data rate at various distances, 10.4--52.4X higher than PLoRa.

Cross-Technology Communication (CTC) is an emerging technique that enables direct communication across different wireless technologies. Recent works achieve physical-level CTC by emulating the standard time-domain waveform of the receiver. This method faces the challenges of inherent unreliability due to the imperfect emulation. Different from analog emulation, we propose a novel concept named digital emulation, which stems from the following insight: The receiver relies on the phase shift to decode symbols rather than the shape of analog time-domain waveform. There are lots of phase sequences which satisfy the requirement of phase shift. The distortions of these phase sequences after WiFi emulation are different. We have the opportunity to select an appropriate phase sequence with the relatively small emulation errors to achieve a reliable CTC. The key point of digital emulation is generic and applicable to a set of CTCs, where the transmitter has a wider bandwidth for emulation and the receiver decoding is based on the phase shift. In this paper, we implement our proposal as WIDE, a physical-level CTC via digital emulation from WiFi to ZigBee. We conduct extensive experiments to evaluate the performance of WIDE. The results show that WIDE significantly improves the Packet Reception Ratio (PRR) from 41.7% to 86.2%, which is 2X of WEBee's, an existing representative physical-level CTC.